Mode of action-based classification and prediction of activity of uncouplers for the screening of chemical inventories.

A new approach for classification of uncouplers of oxidative and photophosphorylation, also suitable for screening of large chemical inventories, is introduced. Earlier fragment-based approaches for this mode of toxic action are limited to phenols but weak acids of extremely diverse chemical classes can act as uncouplers. The proposed approach overcomes the limitation to phenolic uncouplers by combining structural fragments with the global information of physico-chemical descriptors. In a top-down approach to reduce the number of candidate chemicals, firstly substructure definitions for the detection of weak acids were applied. Subsequently, conservative physico-chemical thresholds for the two most important properties for the uncoupling activity were defined: an acid dissociation constant (pK(a)) between 3 and 9, and a sufficiently low energy barrier for the internal permeability of anions (17 kcal/mol). The later was derived from a novel approach to calculate the distribution of compounds across membranes. The combination of structural and physico-chemical criteria allowed a good separation of active from inactive chemicals with high sensitivity (95%) and slightly lower (more than 75%) specificity. Applying this approach to several thousand high and low production volume chemicals retrieved a surprisingly small number of 10 compounds with a predicted excess toxicity above 10. Nevertheless, uncoupling can be an important mode of action as highlighted with several examples ranging from pesticide metabolites to persistent organic compounds.

Evaluation of SARs for the prediction of eye irritation/corrosion potential: structural inclusion rules in the BfR decision support system.

The proposed REACH regulation within the European Union (EU) aims to minimise the number of laboratory animals used for human hazard and risk assessment while ensuring adequate protection of human health and the environment. One way to achieve this goal is to develop non-testing methods, such as (quantitative) structure-activity relationships ([Q]SARs), suitable for identifying toxicological hazard from chemical structure and physicochemical properties alone. A database containing data submitted within the EU New Chemicals Notification procedure was compiled by the German Bundesinstitut für Risikobewertung (BfR). On the basis of these data, the BfR built a decision support system (DSS) for the prediction of several toxicological endpoints. For the prediction of eye irritation and corrosion potential, the DSS contains 31 physicochemical exclusion rules evaluated previously by the European Chemicals Bureau (ECB), and 27 inclusion rules that define structural alerts potentially responsible for eye irritation and/or corrosion. This work summarises the results of a study carried out by the ECB to assess the performance of the BfR structural rulebase. The assessment included: (a) evaluation of the structural alerts by using the training set of 1341 substances with experimental data for eye irritation and corrosion; and (b) external validation by using an independent test set of 199 chemicals. Recommendations are made for the further development of the structural rules in order to increase the overall predictivity of the DSS.

The role of the European Chemicals Bureau in promoting the regulatory use of (Q)SAR methods.

Under the proposed REACH (Registration, Evaluation and Authorisation of CHemicals) legislation, (Q)SAR models and grouping methods (chemical categories and read across approaches) are expected to play a significant role in prioritising industrial chemicals for further assessment, and for filling information gaps for the purposes of classification and labelling, risk assessment and the assessment of persistent, bioaccumulative and toxic (PBT) chemicals. The European Chemicals Bureau (ECB), which is part of the European Commission's Joint Research Centre (JRC), has a well-established role in providing independent scientific and technical advice to European policy makers. The ECB also promotes consensus and capacity building on scientific and technical matters among stakeholders in the Member State authorities and industry. To promote the availability and use of (Q)SARs and related estimation methods, the ECB is carrying out a range of activities, including applied research in computational toxicology, the assessment of (Q)SAR models and methods, the development of technical guidance documents and computational tools, and the organisation of training courses. This article provides an overview of ECB activities on computational toxicology, which are intended to promote the development, validation, acceptance and use of (Q)SARs and related estimation methods, both at the European and international levels.

Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study.

The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the OECD criteria may be met when modeling using this neural network approach.

Validation of a QSAR model for acute toxicity.

In the present study, a quantitative structure--activity relationship (QSAR) model has been developed for predicting acute toxicity to the fathead minnow (Pimephales promelas), the aim being to demonstrate how statistical validation and domain definition are both required to establish model validity and to provide reliable predictions. A dataset of 408 heterogeneous chemicals was modelled by a diverse set of theoretical molecular descriptors by using multivariate linear regression (MLR) and Genetic Algorithm-Variable Subset Selection (GA-VSS). This QSAR model was developed to generate reliable predictions of toxicity for organic chemicals not yet tested, so particular emphasis was given to statistical validity and applicability domain. External validation was performed by using OECD Screening Information Data Set (SIDS) data for 177 High Production Volume (HPV) chemicals, and a good predictivity was obtained (=72.1). The model was evaluated according to the OECD principles for QSAR validation, and compliance with all five principles was established. The model could therefore be useful for the regulatory assessment of chemicals. For example, it could be used to fill data gaps within its chemical domain and contribute to the prioritization of chemicals for aquatic toxicity testing.

Prediction of estrogenicity: validation of a classification model.

(Q)SAR models can be used to reduce animal testing as well as to minimise the testing costs. In particular, classification models have been widely used for estimating endpoints with binary activity. The aim of the present study was to develop and validate a classification-based quantitative structure-activity relationship (QSAR) model for endocrine disruption, based on interpretable mechanistic descriptors related to estrogenic gene activation. The model predicts the presence or absence of estrogenic activity according to a pre-defined cut-off in activity as determined in a recombinant yeast assay. The experimental data was obtained from the literature. A two-descriptor classification model was developed that has the form of a decision tree. The predictivity of the model was evaluated by using an external test set and by taking into account the limitations associated with the applicability domain (AD) of the model. The AD was determined as coverage of the model descriptor space. After removing the compounds present in the training set and the compounds outside of the AD, the overall accuracy of classification of the test chemicals was used to assess the predictivity of the model. In addition, the model was shown to meet the OECD Principles for (Q)SAR Validation, making it potentially useful for regulatory purposes.

QSARS for toxicity to the bacterium Sinorhizobium meliloti.

In the present study, structure-activity relationship (QSAR) models for the prediction of the toxicity to the bacterium Sinorhizobium meliloti have been developed, based on a data set of 140 compounds. The data set is highly heterogeneous both in terms of chemistry and mechanisms of toxic action. For deriving QSARs, chemicals were divided into groups according to mechanism of action and chemical structure. The QSARs derived are considered to be of moderate statistical quality. A baseline effect (relationship between the toxicity and logP), which can be related to non-polar narcosis, was observed. To explain toxicity greater than the baseline toxicity, other structural descriptors were used. The development of models for non-polar and polar narcosis had some success. It appeared that the toxicity of compounds acting by more specific mechanisms of toxic action is difficult to predict. A global QSAR was also developed, which had square of the correlation coefficient r2 = 0.53. A QSAR with reasonable statistical parameters was developed for the aliphatic compounds in the data set (r2 = 0.83). QSARs could not be obtained for the aromatic compounds as a group.

Evaluation of qsars for ecotoxicity: a method for assigning quality and confidence.

Validation of a quantitative structure-activity relationship (QSAR) is now considered as an integral part of its development. Assessment of the quality of a QSAR and the confidence that may be placed in predictions from it are vital to any validation procedure. A number of terms associated with the quality of a QSAR, confidence in that QSAR, or both may be quantified. These terms include the: (1) goodness of fit of the model (r2); (2) predictivity of the model (Q2); (3) stability of the model described as the difference between fit and predictivity (Dfp); (4) number of compounds used in the training set (Nc); (5) number of descriptors used in the model (Nd); (6) range of toxicity values (Tr); (7) number of mechanisms of toxic action covered by the training set (Nm), as well as two factors associated with the biological data-confidence associated with, (8) reproducibility of the data (Rconf) and (9) confidence in the source of the data (Sconf). While all these factors may influence the quality of, and/or confidence in a particular QSAR, each varies within different limits. To enable a quantitative assessment of quality and confidence in a QSAR, the terms deemed to be important were weighed and combined to create a Confidence Index (CI): ((r2)4 x 6) x ((Q2)4 x 6) x (ln(Nc/10)) x (Tr) x (Sconf)0.5 (ln(N2d + 2)) x (ln(N2m + 2)) x ((r2)4 x 6) - ((Q2)4 x 6) + 1) x (Rconf)

Qsar investigation of a large data set for fish, algae and Daphnia toxicity.

A large data set containing values for fish, algae and Daphnia toxicity for more than 2000 chemicals and mixtures was investigated. The data set was taken from the New Chemicals Data Base of the European Union [hosted by the European Chemicals Bureau, Joint Research Centre, European Commission (http://ecb.jrc.it)]. The data are submitted by industry, according to the requirements of EU Council Directive 67/548/EEC as amended for the seventh time by EU Council Directive 92/32/EEC. The toxicities of neutral chemicals, salts, metal complexes, as well as chemical mixtures were extracted. A baseline effect was demonstrated by chemicals known to act by a narcotic mechanism of action, i.e., a relationship was observed between the toxicity and the logarithm of the octanol-water partition coefficient (log P). However, the prediction of the toxicity of more reactive chemicals was found to require the use of additional descriptors.

Partial least squares modelling of the acute toxicity of aliphatic compounds to Tetrahymena pyriformis.

The aim of this study was to evaluate a multivariate statistical model, utilising Partial Least Squares (PLS) analysis, for the prediction of the acute toxicity of aliphatic chemicals to the ciliate Tetrahymena pyriformis. A model was developed that was capable of making a prediction regardless the mechanism of toxic action. The toxicity of 476 compounds, possessing different mechanisms of toxic action was considered. A set of 74 descriptors, including the octanol-water partition coefficient, molecular-orbital descriptors, geometrical, topological and connectivity indices, was generated. A three-component, eight-descriptor PLS model was developed. It was validated by a Y-permutation test and by simulation of external prediction for complementary subsets. A comparison with existing class or mechanism-based models, derived on the same data set, was made.

Selection of data sets for QSARs: analyses of Tetrahymena toxicity from aromatic compounds.

The aim of this investigation was to develop a strategy for the formulation of a valid ecotoxicological-based QSAR while, at the same time, minimizing the required number of toxicological data points. Two chemical selection approaches-distance-based optimality and K Nearest Neighbor (KNN), were used to examine the impact of the number of compounds used in the training and testing phases of QSAR development (i.e. diversity and representivity, respectively) on the predictivity (i.e. external validation) of the QSAR. Regression-based QSARs for the ectotoxic potency for population growth impairment of aromatic compounds (benzenes) to the aquatic ciliate Tetrahymena pyriformis were developed based on descriptors for chemical hydrophobicity and electrophilicity. A ratio of one compound in the training set to three in the test set was applied. The results indicate that from a known chemical universe, in this case 385 derivatives, robust QSARs of equal quality may be developed from a small number of diverse compounds, validated by a representative test set. As a conservative recommendation it is suggested that there should be a minimum of 10 observations for each variable in a QSAR.

2D and 3D QSAR analysis of some valproic acid metabolites and analogues as anticonvulsant agents.

PURPOSE: To investigate the structural features, responsible for the variations in anticonvulsant activity of a series of twenty six valproic acid (VPA) metabolites and analogues. METHODS: Different approaches for quantitative structure--activity relationship analysis (QSAR) as conventional 2D QSAR analysis and comparative molecular field analysis (3D QSAR) were used. The 2D QSAR was performed with more than twenty structure descriptors as the partition and distribution coefficients, topological, geometrical and electronic descriptors, and indicator variables. The electronic descriptors were calculated for the energetically most stable conformers. For the need of 3D QSAR steric and electrostatic potential maps were generated. Partial least squares (PLS) analysis has been carried out for the statistical evaluation of the models and weighted least squares (WLS) analysis was used for the visualization of the results. RESULTS: It was established that the two approaches--2D and 3D QSAR, prove the importance of the lipophilicity of the compounds for anticonvulsant activity. The results from both the approaches suggest that a substitution at alpha-position is essential for a higher activity. CONCLUSIONS: 3D QSAR is useful for describing the steric and electrostatic fields, important for the activity. For predicting the activity of new compounds 2D QSAR tools were proposed.

Cytotoxic activity of cerium complexes with coumarin derivatives. Molecular modeling of the ligands.

Cerium complexes of Umbellipherone, Mendiaxon, Warfarin, Coumachlor, and Niffcoumar have been synthesized by reaction of the ligands with cerium nitrate in a stoichiometric ratio of 1:2. The formation of the complexes has been proved on the basis of elemental analysis, conductivities, IR spectroscopy, and 1H-NMR spectroscopy. The molecules of the ligands were optimized by means of the semiempirical quantum mechanical method PM3 to the energetically most stable conformers. All the ligands were characterized by molecular and submolecular electronic indices and the putative donor centers are proposed. It is concluded that the lactone- and the keto-carbonyl groups of Warfarin, Coumachlor, and Niffcoumar are bonded to the metal ion as bidentate ligands. The other two coumarins are bonded as monodentate ligands. Conductivity measurements show the non-electrolytic nature of the complexes. Cytotoxic screening by MTT assay was carried out. The cerium complexes were found to be more active than the inorganic salts.